CN115274218B - Coaxial cable concentricity online compensation control method and system - Google Patents

Coaxial cable concentricity online compensation control method and system Download PDF

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CN115274218B
CN115274218B CN202211194458.6A CN202211194458A CN115274218B CN 115274218 B CN115274218 B CN 115274218B CN 202211194458 A CN202211194458 A CN 202211194458A CN 115274218 B CN115274218 B CN 115274218B
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concentricity
electric control
state
coaxial cable
water tank
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CN115274218A (en
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王念立
李选进
祝建峰
朱福浩
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Wuhan Changfei General Cable Co ltd
Yangtze Optical Fibre and Cable Co Ltd
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Yangtze Optical Fibre and Cable Co Ltd
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    • H—ELECTRICITY
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    • H01—ELECTRIC ELEMENTS
    • H01B—CABLES; CONDUCTORS; INSULATORS; SELECTION OF MATERIALS FOR THEIR CONDUCTIVE, INSULATING OR DIELECTRIC PROPERTIES
    • H01B13/00—Apparatus or processes specially adapted for manufacturing conductors or cables
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Abstract

The invention discloses a coaxial cable concentricity online compensation control method and system. The method comprises the following steps: (1) Acquiring the thickness of a foaming layer in each radial detection direction of the X-ray eccentricity detector; (2) acquiring the water temperature and the water flow speed of the hot water tank; (3) Acquiring electric control pressure values of all pressure discharge ports of the foaming material extruder; (4) And (4) deciding the electric control pressure value of the pressure discharge hole by adopting a model-free reinforcement learning algorithm based on a Markov process. The system comprises an X-ray deviation measuring instrument, a water tank monitoring module, a discharge port pressure detection module, a decision control module and an extruder head electric control module. The method is combined with an industrial Internet of things data acquisition technology and a Markov process-based model-free reinforcement learning algorithm, online compensation control of the concentricity of the coaxial cable is realized, control compensation is performed on hardware equipment, and with continuous iteration of the algorithm, the concentricity of the coaxial cable can be controlled above a qualified standard, and the concentricity can be stabilized in an optimal mechanical state.

Description

Coaxial cable concentricity online compensation control method and system
Technical Field
The invention belongs to the field of coaxial cable processing, and particularly relates to a coaxial cable concentricity online compensation control method and system.
Background
The radio frequency coaxial cable is a cable which is provided with two concentric conductors, an inner conductor and an outer conductor share the same axis, and the basic structure of the radio frequency coaxial cable comprises an inner conductor, an insulating layer, an outer conductor and an outer sheath. The concentricity refers to the position of a conductor on each insulating layer, the coaxial cable has good coaxial symmetry, thin slices of the insulating layer are taken, the thickness of the thin slices is measured by using a projector, and the numerical value of the insulation concentricity is obtained by calculation.
When the concentricity of the cable insulation layer exceeds the set index or the fluctuation is too large, the indexes of the cable such as electrical performance, impedance, standing wave and the like are obviously deteriorated.
The existing insulation concentricity detection is generally an X-ray deviation tester on-line detection, a worker adjusts the pressure of a discharge port of a foaming material extrusion head according to data, but the skill proficiency of the worker is tested, and when the experience of the worker is insufficient, the adjusted concentricity is possibly worse, and the product quality is influenced. And when the X-ray deviation measuring instrument detects that the concentricity is unqualified, the quality of the product cannot be remedied, and the product can only be discarded.
Meanwhile, the change of the working condition environment can also influence the forming process of the coaxial cable foaming insulating material to a certain extent, thereby influencing the concentricity, and being difficult to make accurate and timely adjustment by artificial observation, thus leading to the reduction of the product quality.
Disclosure of Invention
Aiming at the defects or improvement requirements of the prior art, the invention provides a coaxial cable concentricity online compensation control method and a coaxial cable concentricity online compensation control system, aiming at the foaming layer extrusion process of the assembled coaxial cable production line, iterative update control is carried out by adopting a model-free reinforcement learning algorithm based on a Markov process, so that coaxial cable concentricity online compensation control is realized, concentricity reduction or fluctuation caused by hardware equipment and working condition environment difference is compensated by software compensation, and the technical problem that the concentricity reduction or fluctuation caused by the working condition environment difference cannot be adapted in real time due to the fact that the pressure of a discharge port of an extrusion machine head is manually controlled in the prior art is solved.
In order to achieve the above object, according to one aspect of the present invention, there is provided a coaxial cable concentricity online compensation control method, which is applied to a coaxial cable production line having an X-ray eccentricity detector;
the coaxial cable production line at least comprises a foaming material extruder, a hot water tank and an X-ray polarization measuring instrument based on X-ray measurement, wherein the foaming material extruder, the hot water tank and the X-ray polarization measuring instrument are sequentially arranged in the production line direction after an inner conductor is formed;
the foaming material extruder is provided with a plurality of electrically controlled pressure discharge ports which are uniformly arranged on the circumference of the extruder head in the circumferential direction;
in each time slot
Figure 674353DEST_PATH_IMAGE001
Executing the following steps:
(1) Obtaining the thickness of the foaming layer in each radial detection direction of the X-ray eccentricity detector, and combining the thicknesses into an eccentric state
Figure 627266DEST_PATH_IMAGE002
Is recorded as
Figure 158741DEST_PATH_IMAGE003
In which
Figure 564315DEST_PATH_IMAGE004
For the number of radial detection directions,
Figure 268966DEST_PATH_IMAGE005
is a first
Figure 14068DEST_PATH_IMAGE006
The thickness of the foaming layer measured in the radial detection direction,
Figure 778762DEST_PATH_IMAGE007
；
(2) Obtaining the water temperature of the hot water tank
Figure 406052DEST_PATH_IMAGE008
Velocity of water flow
Figure 586498DEST_PATH_IMAGE009
Combined into a hot water tank
Figure 514002DEST_PATH_IMAGE010
It is recorded as
Figure 387280DEST_PATH_IMAGE011
；
(3) Obtaining the electric control pressure values of all pressure discharge ports of the foaming material extruder, and combining the electric control pressure values into a discharge port state
Figure 767446DEST_PATH_IMAGE012
It is recorded as
Figure 545391DEST_PATH_IMAGE013
Wherein
Figure 265085DEST_PATH_IMAGE014
The number of the discharge holes is the same as that of the discharge holes,
Figure 106002DEST_PATH_IMAGE015
is as follows
Figure 973464DEST_PATH_IMAGE016
The electric control pressure value of the discharge hole,
Figure 558029DEST_PATH_IMAGE017
；
(4) Acquiring the eccentric states according to the steps (1) - (3)
Figure 132230DEST_PATH_IMAGE002
State of hot water tank
Figure 144048DEST_PATH_IMAGE010
State of discharge port
Figure 233227DEST_PATH_IMAGE012
And deciding the next time slot by adopting a model-free reinforcement learning algorithm based on the Markov process
Figure 824745DEST_PATH_IMAGE018
Pressure discharge hole electric control signal
Figure 315769DEST_PATH_IMAGE019
(ii) a Discharge port electric control signal
Figure 498489DEST_PATH_IMAGE019
The electric control signal value of each pressure discharge hole is formed and recorded as
Figure 278226DEST_PATH_IMAGE020
；
(5) Obtaining an electric control signal of the foaming material extruder according to the step (4)
Figure 470173DEST_PATH_IMAGE019
Adjusting the electric control pressure values of a plurality of pressure discharge ports and entering the next time slot
Figure 550125DEST_PATH_IMAGE018
。
Preferably, the coaxial cable concentricity online compensation control method includes the step (4) of a markov process-based model-free reinforcement learning algorithm, which specifically includes the following steps:
state of state
Figure 372587DEST_PATH_IMAGE021
Defined as an eccentric state
Figure 436358DEST_PATH_IMAGE002
State of hot water tank
Figure 900837DEST_PATH_IMAGE010
State of discharge port
Figure 304137DEST_PATH_IMAGE012
Aggregate, written as:
Figure 359818DEST_PATH_IMAGE022
；
action of moving
Figure 582989DEST_PATH_IMAGE023
Is defined as an electric control signal of the pressure discharge hole and recorded as
Figure 116738DEST_PATH_IMAGE024
；
Reward function
Figure 436861DEST_PATH_IMAGE025
The method is defined as the weighted sum of the concentricity and the negative value of the change rate of the electric control pressure value of each discharge port, and comprises the following steps:
Figure 335547DEST_PATH_IMAGE026
wherein, the first and the second end of the pipe are connected with each other,
Figure 373910DEST_PATH_IMAGE027
in order to be a weight coefficient of the image,
Figure 649034DEST_PATH_IMAGE028
for concentricity, the following method is used for calculation:
Figure 561013DEST_PATH_IMAGE029
wherein, the first and the second end of the pipe are connected with each other,
Figure 958497DEST_PATH_IMAGE030
is the maximum value of the thickness of the foamed layer in each radial detection direction,
Figure 156260DEST_PATH_IMAGE031
is the minimum value of the thickness of the foamed layer in each radial detection direction.
Preferably, in the coaxial cable concentricity online compensation control method, the DQN network is adopted in step (4) to maximize decision utility.
Preferably, the coaxial cable concentricity online compensation control method and the strategy thereof
Figure 297391DEST_PATH_IMAGE032
Is in a given state
Figure 326527DEST_PATH_IMAGE033
Selecting an action
Figure 567016DEST_PATH_IMAGE023
Function of probability with the goal of maximizing the slave time
Figure 579971DEST_PATH_IMAGE034
Starting the value of the prize accumulated over the previous preset time period;
on-policy
Figure 196897DEST_PATH_IMAGE032
Down-defined action value function
Figure 346119DEST_PATH_IMAGE035
The following were used:
Figure 554246DEST_PATH_IMAGE036
wherein, the first and the second end of the pipe are connected with each other,
Figure 726601DEST_PATH_IMAGE037
the value of the discount factor is represented by,
Figure 209535DEST_PATH_IMAGE038
is a mathematical expectation;
optimal action value
Figure 150947DEST_PATH_IMAGE035
The following equation is satisfied:
Figure 61134DEST_PATH_IMAGE039
in learning algorithms, the invention uses
Figure 517523DEST_PATH_IMAGE035
Is
Figure 476252DEST_PATH_IMAGE040
To estimate an optimal action value function
Figure 600065DEST_PATH_IMAGE035
，
Figure 618837DEST_PATH_IMAGE040
The table update rules are as follows:
Figure 296943DEST_PATH_IMAGE041
wherein the content of the first and second substances,
Figure 121680DEST_PATH_IMAGE042
is the learning rate.
Preferably, the coaxial cable concentricity online compensation control method is used for detecting the number of directions in the radial direction
Figure 37683DEST_PATH_IMAGE043
Number of said discharge ports
Figure 289673DEST_PATH_IMAGE044
。
According to another aspect of the present invention, there is provided a coaxial cable concentricity in-line compensation control system, comprising:
the device comprises an X-ray deviation measuring instrument, a water tank monitoring module, a discharge port pressure detection module, a decision control module and an extruder head electric control module;
the X-ray polarization measuring instrument is used for monitoring and acquiring the thickness of the foaming layer in each radial detection direction of the X-ray polarization center detector and combining the foaming layer into an eccentric state
Figure 923916DEST_PATH_IMAGE002
And submitted to the decision control module to be recorded
Figure 21185DEST_PATH_IMAGE045
Wherein
Figure 854012DEST_PATH_IMAGE046
For the number of radial detection directions,
Figure 214586DEST_PATH_IMAGE005
is a first
Figure 153372DEST_PATH_IMAGE006
The thickness of the foaming layer measured in the radial detection direction,
Figure 54332DEST_PATH_IMAGE047
；
the water tank monitoring module is used for monitoring and acquiring the water temperature of the hot water tank
Figure 944927DEST_PATH_IMAGE008
Velocity of water flow
Figure 538720DEST_PATH_IMAGE048
Combined into a hot water tank
Figure 881976DEST_PATH_IMAGE010
And submitted to the decision control module to be recorded
Figure 586627DEST_PATH_IMAGE011
；
The discharge port pressure detection module is used for monitoring and acquiring electric control pressure values of all pressure discharge ports of the foaming material extruder to form a discharge port state
Figure 128467DEST_PATH_IMAGE012
And submitted to the decision control module to be recorded
Figure 830844DEST_PATH_IMAGE013
Wherein
Figure 723713DEST_PATH_IMAGE049
The number of the discharge holes is the same as that of the discharge holes,
Figure 169738DEST_PATH_IMAGE050
is as follows
Figure 831664DEST_PATH_IMAGE051
An electric control pressure value of the discharge hole,
Figure 767259DEST_PATH_IMAGE017
；
the decision control module is used for acquiring the eccentric state
Figure 819528DEST_PATH_IMAGE002
State of hot water tank
Figure 131561DEST_PATH_IMAGE010
State of discharge port
Figure 851255DEST_PATH_IMAGE012
And deciding the next time slot by adopting a model-free reinforcement learning algorithm based on the Markov process
Figure 957751DEST_PATH_IMAGE052
Pressure discharge hole electric control signal
Figure 294055DEST_PATH_IMAGE019
And submitting to an extruder head electric control module; electric control signal of discharge port
Figure 81882DEST_PATH_IMAGE019
The electric control signal value of each pressure discharge port is recorded as
Figure 718400DEST_PATH_IMAGE020
；
Preferably, in the coaxial cable concentricity online compensation control system, the decision control module adopts a markov process-based model-free reinforcement learning algorithm, which specifically includes the following steps:
status of state
Figure 730218DEST_PATH_IMAGE054
Defined as an eccentric state
Figure 22659DEST_PATH_IMAGE002
State of hot water tank
Figure 410915DEST_PATH_IMAGE010
State of the discharge port
Figure 105202DEST_PATH_IMAGE012
Set, recorded as:
Figure 287922DEST_PATH_IMAGE022
；
action of moving
Figure 726381DEST_PATH_IMAGE023
Defined as the electric control signal of the pressure discharge hole and recorded as
Figure 652748DEST_PATH_IMAGE024
；
Reward function
Figure 263858DEST_PATH_IMAGE055
The method is defined as the weighted sum of the concentricity and the negative value of the change rate of the electric control pressure value of each discharge port, and comprises the following steps:
Figure 820742DEST_PATH_IMAGE026
wherein, the first and the second end of the pipe are connected with each other,
Figure 884512DEST_PATH_IMAGE056
in order to be the weight coefficient,
Figure 614571DEST_PATH_IMAGE057
for concentricity, the following method is used for calculation:
Figure 17871DEST_PATH_IMAGE029
wherein the content of the first and second substances,
Figure 807972DEST_PATH_IMAGE030
is the maximum value of the thickness of the foamed layer in each radial detection direction,
Figure 296722DEST_PATH_IMAGE031
is the minimum value of the thickness of the foamed layer in each radial detection direction.
Preferably, in the coaxial cable concentricity online compensation control system, the decision control module of the system maximizes decision utility by using a DQN network.
Preferably, the coaxial cable concentricity online compensation control system and the strategy thereof
Figure 96051DEST_PATH_IMAGE032
Is in a given state
Figure 353857DEST_PATH_IMAGE033
Selecting an action
Figure 49281DEST_PATH_IMAGE023
Function of probability with the goal of maximizing time from
Figure 87644DEST_PATH_IMAGE058
Starting the value of the prize accumulated in the previous preset time period;
on-policy
Figure 362767DEST_PATH_IMAGE032
Down-defined action value function
Figure 130872DEST_PATH_IMAGE035
The following:
Figure 466038DEST_PATH_IMAGE036
wherein, the first and the second end of the pipe are connected with each other,
Figure 457609DEST_PATH_IMAGE059
the value of the discount factor is represented by,
Figure 598741DEST_PATH_IMAGE060
is a mathematical expectation;
optimal action value
Figure 565560DEST_PATH_IMAGE035
The following equation is satisfied:
Figure 868365DEST_PATH_IMAGE039
in learning algorithms, the invention uses
Figure 615741DEST_PATH_IMAGE035
Is
Figure 232667DEST_PATH_IMAGE061
To estimate an optimal action value function
Figure 381889DEST_PATH_IMAGE035
，
Figure 58858DEST_PATH_IMAGE061
The table update rules are as follows:
Figure 27951DEST_PATH_IMAGE041
wherein, the first and the second end of the pipe are connected with each other,
Figure 510885DEST_PATH_IMAGE042
is the learning rate.
Preferably, the coaxial cable concentricity online compensation control system is used for detecting the number of directions in the radial direction
Figure 452296DEST_PATH_IMAGE043
Number of discharge ports
Figure 362483DEST_PATH_IMAGE062
。
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
the method is combined with an industrial Internet of things data acquisition technology and a Markov process-based model-free reinforcement learning algorithm, online compensation control of the concentricity of the coaxial cable is realized, control compensation is performed on hardware equipment, and along with continuous iteration of the algorithm, the concentricity of the coaxial cable can be controlled above a qualified standard, the concentricity can be stabilized in an optimal mechanical state, and concentricity fluctuation is reduced; and can be automatically adapted to different working condition environments.
Drawings
FIG. 1 is a schematic view of a coaxial cable production line module employed in an embodiment of the present invention;
FIG. 2 is a view showing a production line direction projection of a foamed material extruder according to an embodiment of the present invention;
fig. 3 is a schematic view of the radial detection direction of the X-ray polarization detector in the embodiment of the invention.
The same reference numbers will be used throughout the drawings to refer to the same or like elements or structures, wherein: the device comprises an X-ray deviation measuring instrument 1, a water tank monitoring module 2, an intelligent gateway 3, a foaming material extruder 4, a pressure discharge hole 401 and an extruder head 402.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The coaxial cable concentricity on-line compensation control method provided by the invention is applied to a coaxial cable production line with an X-ray eccentricity detector as shown in figure 1;
the coaxial cable production line at least comprises a foaming material extruder, a hot water tank and an X-ray polarization measuring instrument based on X-ray measurement, wherein the foaming material extruder, the hot water tank and the X-ray polarization measuring instrument are sequentially arranged in the production line direction after an inner conductor is formed;
the production line direction projection view of the foaming material extruder is shown in fig. 2, and the foaming material extruder is provided with a plurality of electrically controlled pressure discharge ports which are uniformly arranged on the circumference of an extruder head in the circumferential direction;
in each time slot
Figure 818873DEST_PATH_IMAGE001
Executing the following steps:
(1) Obtaining the thickness of the foaming layer in each radial detection direction of the X-ray eccentricity detector, and combining the thicknesses into an eccentric state
Figure 777601DEST_PATH_IMAGE002
It is recorded as
Figure 901415DEST_PATH_IMAGE045
In which
Figure 654607DEST_PATH_IMAGE004
For the number of radial detection directions,
Figure 863872DEST_PATH_IMAGE005
is a first
Figure 423029DEST_PATH_IMAGE006
The thickness of the foaming layer measured in the radial detection direction,
Figure 339033DEST_PATH_IMAGE063
(ii) a In general, the X-ray eccentricity detector,
Figure 591022DEST_PATH_IMAGE064
preferably, the thickness of the foamed layer is measured radially from 8 degrees, i.e.
Figure 959687DEST_PATH_IMAGE065
；
(2) Obtaining the water temperature of the hot water tank
Figure 56956DEST_PATH_IMAGE066
Water flow velocityDegree of rotation
Figure 155362DEST_PATH_IMAGE048
Combined into a hot water tank
Figure 515936DEST_PATH_IMAGE010
Is recorded as
Figure 434213DEST_PATH_IMAGE011
；
(3) Obtaining the electric control pressure values of all pressure discharge ports of the foaming material extruder, and combining the electric control pressure values into a discharge port state
Figure 538436DEST_PATH_IMAGE012
It is recorded as
Figure 228699DEST_PATH_IMAGE013
Wherein
Figure 822491DEST_PATH_IMAGE067
The number of the discharge holes is the same as that of the discharge holes,
Figure 165748DEST_PATH_IMAGE068
is as follows
Figure 604819DEST_PATH_IMAGE069
An electric control pressure value of the discharge hole,
Figure 412238DEST_PATH_IMAGE070
；
preferably, the foaming material extruder is provided with 4 pressure discharge ports, namely
Figure 114615DEST_PATH_IMAGE071
；
(4) The eccentric state collected according to the steps (1) to (3)
Figure 7485DEST_PATH_IMAGE002
State of hot water tank
Figure 453510DEST_PATH_IMAGE010
State of discharge port
Figure 849856DEST_PATH_IMAGE012
Adopting a model-free reinforcement learning algorithm based on the Markov process to decide the next time slot
Figure 785451DEST_PATH_IMAGE018
Pressure discharge hole electric control signal
Figure 103300DEST_PATH_IMAGE019
(ii) a Discharge port electric control signal
Figure 415332DEST_PATH_IMAGE019
The electric control signal value of each pressure discharge port is recorded as
Figure 869447DEST_PATH_IMAGE020
；
In the production process of the coaxial cable, the most important influence on the concentricity is the uniformity degree of all thicknesses of the foaming layer. The foaming layer is produced by extruding the heated material out of the inner conductor, and foaming and shaping the heated material by a hot water tank to form the foaming layer. However, the foaming process is complicated and is affected by the extrusion process and the heat-insulating process, the uniformity of the extruded material, the water temperature and the flow rate of the hot water tank, and the thickness of each phase of the foaming layer, thereby affecting the concentricity, and the influence is complicated, changes along with the environmental conditions, is specific to each production line, and is sensitive to the foaming material. For example, when the environmental temperatures of the production lines are different in different seasons, the temperature change of the hot water tank in the whole process is different, and the foaming process is influenced; the specific design of the extrusion head, the length of the hot water bath, and the composition of the foaming material used, all affect the foaming process for the production line. Therefore, for the adjustment of the concentricity, the adjustment can be carried out only by depending on the long-term adjustment experience of workers at present, the automatic on-line compensation of the concentricity cannot be realized, and when the decrease of the concentricity is detected, the manual adjustment is carried out, and the quality reduction of the cable is inevitable.
The Markov Decision Process (MDP) is a mathematical model of sequential Decision for simulating the randomness strategy and reward achievable by a smart in an environment where the system state has Markov properties. The method is based on a Markov process model-free reinforcement learning algorithm, decides the electric control signal of the foaming material extruder, can adjust the accuracy of signal control through multiple iterations, continuously updates the adjustment strategy according to the reward function, and iterates to adapt to the current working condition environment while obtaining high concentricity, thereby realizing automatic concentricity online compensation.
Specifically, the model-free reinforcement learning algorithm based on the Markov process adopted by the invention specifically comprises the following steps:
state of state
Figure 710364DEST_PATH_IMAGE072
Defined as an eccentric state
Figure 577826DEST_PATH_IMAGE002
State of hot water tank
Figure 365654DEST_PATH_IMAGE010
State of the discharge port
Figure 2171DEST_PATH_IMAGE012
Set, recorded as:
Figure 13990DEST_PATH_IMAGE022
；
action of moving
Figure 306431DEST_PATH_IMAGE073
Is defined as an electric control signal of the pressure discharge hole and recorded as
Figure 694687DEST_PATH_IMAGE024
；
Reward function
Figure 123394DEST_PATH_IMAGE074
The method is defined as the concentricity and the weighted sum of the negative values of the electric control pressure values of all the discharge ports, and the calculation method comprises the following steps:
Figure 837272DEST_PATH_IMAGE026
wherein the content of the first and second substances,
Figure 351430DEST_PATH_IMAGE075
in order to be the weight coefficient,
Figure 277798DEST_PATH_IMAGE076
for concentricity, the following method is used for calculation:
Figure 888908DEST_PATH_IMAGE029
wherein, the first and the second end of the pipe are connected with each other,
Figure 445791DEST_PATH_IMAGE030
is the maximum value of the thickness of the foamed layer in each radial detection direction,
Figure 241053DEST_PATH_IMAGE031
is the minimum value of the thickness of the foamed layer in each radial detection direction.
In the actual on-line concentricity compensation, on one hand, the concentricity is pursued, on the other hand, the overall foaming layer process is expected to be relatively stable, and the influence of other processes by a concentricity compensation algorithm is avoided; i.e. the problem of excessive variation in the extrusion outlet pressure despite the high concentricity needs to be avoided. In order to obtain the maximum long-term utility, the online compensation control method tries to improve the concentricity and stabilize the electric control pressure value of each discharge port, and forms stable automatic intelligent concentricity negative feedback compensation control through long-term iteration.
Preferably, the DQN network is used to maximize decision utility, as follows:
its strategy
Figure 236691DEST_PATH_IMAGE032
Is in a given state
Figure 639991DEST_PATH_IMAGE033
Selecting an action
Figure 430092DEST_PATH_IMAGE023
Function of probability with the goal of maximizing time from
Figure 918842DEST_PATH_IMAGE034
The accumulated reward value in the preset time period before starting, thereby avoiding the problem that the production environment changes violently before the accumulation time is too long, which leads to the delay of strategy updating.
In the policy
Figure 452592DEST_PATH_IMAGE032
Down-defined action value function
Figure 507135DEST_PATH_IMAGE035
The following:
Figure 671400DEST_PATH_IMAGE036
wherein, the first and the second end of the pipe are connected with each other,
Figure 709764DEST_PATH_IMAGE077
a discount factor is indicated in the form of a discount,
Figure 719308DEST_PATH_IMAGE078
is a mathematical expectation.
Optimal action value
Figure 893937DEST_PATH_IMAGE035
The following equation is satisfied:
Figure 25841DEST_PATH_IMAGE039
in learning algorithms, the invention uses
Figure 489184DEST_PATH_IMAGE035
Is/are as follows
Figure 364736DEST_PATH_IMAGE061
To estimate an optimal action value function
Figure 597134DEST_PATH_IMAGE035
，
Figure 165519DEST_PATH_IMAGE061
The table update rules are as follows:
Figure 647316DEST_PATH_IMAGE041
wherein the content of the first and second substances,
Figure 264242DEST_PATH_IMAGE042
is the learning rate.
(5) Obtaining an electric control signal of the foaming material extruder according to the step (4)
Figure 413463DEST_PATH_IMAGE079
Adjusting the electric control pressure values of the plurality of pressure discharge ports and entering the next time slot
Figure 887170DEST_PATH_IMAGE018
。
The coaxial cable concentricity online compensation control method provided by the invention comprises the following steps: in the time slot
Figure 59525DEST_PATH_IMAGE001
The control device acquires the current state
Figure 276880DEST_PATH_IMAGE033
Action of use
Figure 483870DEST_PATH_IMAGE023
Determining a next time slot
Figure 394058DEST_PATH_IMAGE018
Electric control signal of foaming material extruder
Figure 850447DEST_PATH_IMAGE019
Then obtain the reward from the environment
Figure 809175DEST_PATH_IMAGE080
Then the state space is passed on to the next state
Figure 670340DEST_PATH_IMAGE081
Use of
Figure 485849DEST_PATH_IMAGE082
Updating
Figure 632797DEST_PATH_IMAGE083
The value is obtained.
The invention provides a coaxial cable concentricity online compensation control system, which comprises:
the device comprises an X-ray deviation measuring instrument, a water tank monitoring module, a discharge port pressure detection module, a decision control module and an extruder head electric control module;
the X-ray polarization measuring instrument is used for monitoring and acquiring the thickness of the foaming layer in each radial detection direction of the X-ray polarization center detector and combining the foaming layer into an eccentric state
Figure 457533DEST_PATH_IMAGE002
And submitted to the decision control module to be recorded
Figure 373537DEST_PATH_IMAGE045
In which
Figure 359947DEST_PATH_IMAGE004
For the number of radial detection directions,
Figure 790928DEST_PATH_IMAGE005
is a first
Figure 91460DEST_PATH_IMAGE006
The thickness of the foaming layer measured in the radial detection direction,
Figure 189866DEST_PATH_IMAGE084
；
the water tank monitoring module is used for monitoring and acquiring the water temperature of the hot water tank
Figure 284861DEST_PATH_IMAGE008
Velocity of water flow
Figure 203138DEST_PATH_IMAGE085
Combined into a hot water tank
Figure 369677DEST_PATH_IMAGE010
And submitted to the decision control module to be recorded
Figure 994694DEST_PATH_IMAGE011
；
The discharge port pressure detection module is used for monitoring and acquiring electric control pressure values of all pressure discharge ports of the foaming material extruder to form a discharge port state
Figure 588486DEST_PATH_IMAGE086
And submitted to the decision control module to be recorded
Figure 931743DEST_PATH_IMAGE013
In which
Figure 901973DEST_PATH_IMAGE087
The number of the discharge holes is the same as that of the discharge holes,
Figure 443813DEST_PATH_IMAGE088
is as follows
Figure 146189DEST_PATH_IMAGE089
The electric control pressure value of the discharge hole,
Figure 39059DEST_PATH_IMAGE090
；
the decision control module is used for acquiring the eccentric state
Figure 281821DEST_PATH_IMAGE002
State of hot water tank
Figure 881430DEST_PATH_IMAGE091
State of discharge port
Figure 817025DEST_PATH_IMAGE012
Adopting a model-free reinforcement learning algorithm based on the Markov process to decide the next time slot
Figure 134874DEST_PATH_IMAGE018
Pressure discharge hole electric control signal
Figure 181327DEST_PATH_IMAGE019
And submitting to an extruder head electric control module; discharge port electric control signal
Figure 901022DEST_PATH_IMAGE019
The electric control signal value of each pressure discharge hole is formed and recorded as
Figure 741939DEST_PATH_IMAGE020
；
Specifically, the model-free reinforcement learning algorithm based on the Markov process adopted by the invention specifically comprises the following steps:
state of state
Figure 606471DEST_PATH_IMAGE092
Defined as an eccentric state
Figure 394298DEST_PATH_IMAGE002
State of hot water tank
Figure 30816DEST_PATH_IMAGE010
State of the discharge port
Figure 42634DEST_PATH_IMAGE012
Set, recorded as:
Figure 69496DEST_PATH_IMAGE022
；
action
Figure 723331DEST_PATH_IMAGE093
Defined as the electric control signal of the pressure discharge hole and recorded as
Figure 152039DEST_PATH_IMAGE024
；
Reward function
Figure 600338DEST_PATH_IMAGE094
The method is defined as the concentricity and the weighted sum of the negative values of the electric control pressure values of all the discharge ports, and the calculation method comprises the following steps:
Figure 176812DEST_PATH_IMAGE026
wherein the content of the first and second substances,
Figure 306442DEST_PATH_IMAGE095
in order to be the weight coefficient,
Figure 651973DEST_PATH_IMAGE096
the concentricity ss degree is calculated according to the following method:
Figure 474436DEST_PATH_IMAGE029
wherein, the first and the second end of the pipe are connected with each other,
Figure 272627DEST_PATH_IMAGE030
is the maximum value of the thickness of the foamed layer in each radial detection direction,
Figure 268265DEST_PATH_IMAGE031
is the minimum value of the thickness of the foamed layer in each radial detection direction.
Preferably, the DQN network is used to maximize decision utility, as follows:
its strategy
Figure 405986DEST_PATH_IMAGE032
Is in a given state
Figure 461666DEST_PATH_IMAGE033
Selecting an action
Figure 950416DEST_PATH_IMAGE023
Function of probability with the goal of maximizing time from
Figure 484166DEST_PATH_IMAGE034
The accumulated reward value in the preset time period before starting, thereby avoiding the problem that the production environment changes violently before the accumulation time is too long, which leads to the delay of strategy updating.
On-policy
Figure 538710DEST_PATH_IMAGE032
Function of lower definition action value
Figure 702975DEST_PATH_IMAGE035
The following were used:
Figure 475759DEST_PATH_IMAGE036
wherein the content of the first and second substances,
Figure 750882DEST_PATH_IMAGE097
the value of the discount factor is represented by,
Figure 925512DEST_PATH_IMAGE098
is a mathematical expectation.
Optimal action value
Figure 57416DEST_PATH_IMAGE035
The following equation is satisfied:
Figure 520758DEST_PATH_IMAGE039
in learning algorithms, the invention uses
Figure 396310DEST_PATH_IMAGE035
Is
Figure 628708DEST_PATH_IMAGE099
To estimate an optimal action value function
Figure 668864DEST_PATH_IMAGE035
，
Figure 681819DEST_PATH_IMAGE099
The update rule of the table is as follows
Figure 298746DEST_PATH_IMAGE041
Wherein, the first and the second end of the pipe are connected with each other,
Figure 447967DEST_PATH_IMAGE042
is the learning rate.
The extruder head electric control module is used for pressing the electric control signal of the foaming material extruder
Figure 921674DEST_PATH_IMAGE100
And adjusting the electric control pressure values of the plurality of pressure discharge ports.
The following are examples:
adding concentricity on-line compensation control on a coaxial cable production line, as shown in figure 1:
the X-ray polarization measuring instrument for X-ray measurement monitors the thickness of the foaming layer shaped coaxial cable at the outlet of the hot water tank on line in real time in each direction, and the X-ray polarization measuring instrument for X-ray measurement of the embodiment has 8 radial detection directions which are uniformly distributed along the circumference of the coaxial cable, and the interval angle is 45 degrees, as shown in fig. 3;
the water tank monitoring module is arranged at the inlet of the water tank and comprises a temperature sensor and a fluid meter which are respectively used for measuring the water temperature and the water flow speed of the hot water tank. Based on the idea of the invention that software compensation is iteratively adapted to hardware equipment, the sensor is arranged without requiring precise fixation, and can also be arranged at any point in the groove.
And the discharge port pressure detection module is arranged at the discharge port of the extruder and comprises pressure sensors with the installation standards as consistent as possible, and each discharge port is provided with one pressure sensor. Similarly, based on the principle of software compensation, even if the setting standards of the pressure sensors are not completely consistent, a good control effect can be achieved through iterative adaptation, but the pressure sensors which are uniformly set as much as possible can still provide an accurate reward function calculation value, so that the concentricity can be adjusted to a stable control state more quickly.
The data collected by the modules are submitted to an edge computing node arranged on the intelligent gateway through the industrial Internet of things, the edge computing node is used as a decision control module, and a decision signal is issued to the electric control module of the extruder head through the industrial Internet of things.
The extruder head electric control module respectively adjusts electric control voltage of the discharge ports, so that the electric control pressure value of each discharge port is independently adjusted, and the concentricity is stabilized in the optimal state in dynamic adjustment.
The coaxial cable concentricity online compensation control method of the embodiment is used for compensating the control signal of the coaxial cable concentricity online compensation control signal in each time slot
Figure 94029DEST_PATH_IMAGE101
Executing the following steps:
(1) The X-ray deviation measuring instrument obtains the thickness of the foaming layer in each radial detection direction on line and combines the thicknesses into an eccentric state
Figure 311384DEST_PATH_IMAGE002
It is recorded as
Figure 518374DEST_PATH_IMAGE045
Wherein
Figure 162982DEST_PATH_IMAGE004
For the number of radial detection directions,
Figure 619371DEST_PATH_IMAGE005
is as follows
Figure 578100DEST_PATH_IMAGE006
The thickness of the foaming layer measured in the radial direction,
Figure 701914DEST_PATH_IMAGE102
；
(2) The water tank monitoring module acquires the water temperature of the hot water tank
Figure 720686DEST_PATH_IMAGE008
Water flow velocity
Figure 664371DEST_PATH_IMAGE009
Combined into a hot water tank
Figure 223528DEST_PATH_IMAGE010
Is recorded as
Figure 139532DEST_PATH_IMAGE011
；
(3) The discharge port pressure detection module acquires electric control pressure values of all pressure discharge ports of the foaming material extruder to form a discharge port state
Figure 391521DEST_PATH_IMAGE012
It is recorded as
Figure 822503DEST_PATH_IMAGE013
Wherein
Figure 123034DEST_PATH_IMAGE103
The number of the discharge holes is the same as that of the discharge holes,
Figure 955861DEST_PATH_IMAGE104
is a first
Figure 316435DEST_PATH_IMAGE105
An electric control pressure value of the discharge hole,
Figure 500292DEST_PATH_IMAGE106
(ii) a Preferably, the foaming material extruder is provided with 4 pressure discharge ports, namely
Figure 135672DEST_PATH_IMAGE107
；
(4) The decision control module collects the eccentric state according to the steps (1) - (3)
Figure 26268DEST_PATH_IMAGE002
State of hot water tank
Figure 620060DEST_PATH_IMAGE010
State of discharge port
Figure 963317DEST_PATH_IMAGE012
Adopting a model-free reinforcement learning algorithm based on the Markov process to decide the next time slot
Figure 688476DEST_PATH_IMAGE018
Pressure discharge port electric control signal
Figure 495895DEST_PATH_IMAGE019
(ii) a Electric control signal of discharge port
Figure 198271DEST_PATH_IMAGE019
The electric control signal value of each pressure discharge port is recorded as
Figure 91141DEST_PATH_IMAGE020
；
Specifically, the model-free reinforcement learning algorithm based on the markov process adopted in this embodiment specifically includes the following steps:
status of state
Figure 271587DEST_PATH_IMAGE108
Defined as an eccentric state
Figure 199091DEST_PATH_IMAGE002
State of hot water tank
Figure 869107DEST_PATH_IMAGE010
State of discharge port
Figure 186956DEST_PATH_IMAGE012
Aggregate, written as:
Figure 233409DEST_PATH_IMAGE022
；
action
Figure 953104DEST_PATH_IMAGE109
Defined as the electric control signal of the pressure discharge hole and recorded as
Figure 794021DEST_PATH_IMAGE024
；
Reward function
Figure 661483DEST_PATH_IMAGE110
The method is defined as the weighted sum of the concentricity and the negative value of the change rate of the electric control pressure value of each discharge port, and comprises the following steps:
Figure 449310DEST_PATH_IMAGE026
wherein, the first and the second end of the pipe are connected with each other,
Figure 85828DEST_PATH_IMAGE111
in order to be a weight coefficient of the image,
Figure 100002_DEST_PATH_IMAGE112
for concentricity, the following method is used:
Figure 97646DEST_PATH_IMAGE029
wherein the content of the first and second substances,
Figure 921246DEST_PATH_IMAGE030
the maximum value of the thickness of the foamed layer in each radial detection direction,
Figure 512764DEST_PATH_IMAGE031
is the minimum value of the thickness of the foamed layer in each radial detection direction.
The embodiment maximizes the decision utility by using the DQN network, which specifically includes:
its strategy
Figure 3788DEST_PATH_IMAGE032
Is in a given state
Figure 389770DEST_PATH_IMAGE033
Selecting an action
Figure 966245DEST_PATH_IMAGE023
Function of probability with the goal of maximizing time from
Figure 158192DEST_PATH_IMAGE034
And starting the accumulated reward value in the preset time period before, thereby avoiding the problem that the environment changes violently before production to cause the delay of strategy updating due to overlong accumulation time.
On-policy
Figure 441406DEST_PATH_IMAGE032
Down-defined action value function
Figure 60606DEST_PATH_IMAGE035
The following were used:
Figure 124377DEST_PATH_IMAGE036
wherein the content of the first and second substances,
Figure 792119DEST_PATH_IMAGE113
a discount factor is indicated in the form of a discount,
Figure DEST_PATH_IMAGE114
is a mathematical expectation.
Optimal action value
Figure 791823DEST_PATH_IMAGE035
The following equation is satisfied:
Figure 785187DEST_PATH_IMAGE039
in learning algorithms, the invention uses
Figure 70675DEST_PATH_IMAGE035
Is
Figure 807687DEST_PATH_IMAGE061
To estimate an optimal action value function
Figure 862230DEST_PATH_IMAGE035
，
Figure 823233DEST_PATH_IMAGE061
The update rules for the table are as follows:
Figure 799279DEST_PATH_IMAGE041
wherein, the first and the second end of the pipe are connected with each other,
Figure 136720DEST_PATH_IMAGE042
is the learning rate.
(5) Obtaining an electric control signal of the foaming material extruder according to the step (4)
Figure 45770DEST_PATH_IMAGE115
Adjusting the electric control pressure values of a plurality of pressure discharge ports and entering the next time slot
Figure 380936DEST_PATH_IMAGE018
。
The coaxial cable concentricity online compensation control method provided by the invention comprises the following steps: in the time slot
Figure 641016DEST_PATH_IMAGE001
The control device acquires the current state
Figure 719831DEST_PATH_IMAGE033
Action of use
Figure 14546DEST_PATH_IMAGE023
Determining a next time slot
Figure 51772DEST_PATH_IMAGE018
Electric control signal of foaming material extruder
Figure 2410DEST_PATH_IMAGE019
Then obtain the reward from the environment
Figure DEST_PATH_IMAGE116
Then the state space is passed on to the next state
Figure 416074DEST_PATH_IMAGE117
Use of
Figure DEST_PATH_IMAGE118
Updating
Figure 96454DEST_PATH_IMAGE061
The value is obtained.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (10)

1. A coaxial cable concentricity online compensation control method is characterized by being applied to a coaxial cable production line with an X-ray eccentricity detector;
the coaxial cable production line at least comprises a foaming material extruder, a hot water tank and an X-ray polarization measuring instrument based on X-ray measurement, wherein the foaming material extruder, the hot water tank and the X-ray polarization measuring instrument are sequentially arranged in the production line direction after an inner conductor is formed;
the foaming material extruder is provided with a plurality of electrically controlled pressure discharge ports which are uniformly arranged on the circumference of the extruder head in the circumferential direction;
in each time slot
Figure 318974DEST_PATH_IMAGE001
The following steps are executed:
(1) Obtaining the thickness of the foaming layer in each radial detection direction of the X-ray eccentricity detector, and combining the thicknesses into an eccentricity state
Figure 848175DEST_PATH_IMAGE002
It is recorded as
Figure 8373DEST_PATH_IMAGE003
Wherein
Figure 659935DEST_PATH_IMAGE005
For the number of radial detection directions,
Figure 174093DEST_PATH_IMAGE007
is as follows
Figure 38143DEST_PATH_IMAGE009
The thickness of the foaming layer measured in the radial direction,
Figure 586936DEST_PATH_IMAGE010
；
(2) Obtaining the water temperature of the hot water tank
Figure 143820DEST_PATH_IMAGE011
Velocity of water flow
Figure 879695DEST_PATH_IMAGE012
Combined into a hot water tank
Figure 813015DEST_PATH_IMAGE013
It is recorded as
Figure 216315DEST_PATH_IMAGE014
；
(3) Acquiring electric control pressure values of all pressure discharge ports of the foaming material extruder, and combining the electric control pressure values into a discharge port state
Figure 944100DEST_PATH_IMAGE015
It is recorded as
Figure 167270DEST_PATH_IMAGE016
In which
Figure 638703DEST_PATH_IMAGE018
The number of the discharge holes is the same as that of the discharge holes,
Figure 896509DEST_PATH_IMAGE019
is a first
Figure 60774DEST_PATH_IMAGE021
An electric control pressure value of the discharge hole,
Figure 771241DEST_PATH_IMAGE022
；
(4) Acquiring the eccentric states according to the steps (1) - (3)
Figure 515206DEST_PATH_IMAGE002
State of hot water tank
Figure 893098DEST_PATH_IMAGE013
State of discharge port
Figure 697106DEST_PATH_IMAGE023
And deciding the next time slot by adopting a model-free reinforcement learning algorithm based on the Markov process
Figure 160448DEST_PATH_IMAGE024
Pressure discharge hole electric control signal
Figure 973684DEST_PATH_IMAGE025
(ii) a Discharge port electric control signal
Figure 206082DEST_PATH_IMAGE026
Is composed of the electric control signal values of each pressure discharge port and is recorded as
Figure 712150DEST_PATH_IMAGE027
；
(5) Obtaining an electric control signal of the foaming material extruder according to the step (4)
Figure 131630DEST_PATH_IMAGE028
Adjusting the electric control pressure values of a plurality of pressure discharge ports and entering the next time slot
Figure 748556DEST_PATH_IMAGE029
。
2. The coaxial cable concentricity online compensation control method of claim 1, wherein the step (4) is based on a markov process model-free reinforcement learning algorithm, and specifically comprises the following steps:
status of state
Figure 832531DEST_PATH_IMAGE030
Defined as an eccentric state
Figure 509500DEST_PATH_IMAGE002
State of hot water tank
Figure 416276DEST_PATH_IMAGE013
State of the discharge port
Figure 571314DEST_PATH_IMAGE023
Set, recorded as:
Figure 778304DEST_PATH_IMAGE031
；
action of moving
Figure 626174DEST_PATH_IMAGE032
Is defined as an electric control signal of the pressure discharge hole and recorded as
Figure 285826DEST_PATH_IMAGE033
；
Reward function
Figure 978975DEST_PATH_IMAGE035
The method is defined as the concentricity and the weighted sum of the negative values of the electric control pressure values of all the discharge ports, and the calculation method comprises the following steps:
Figure 774893DEST_PATH_IMAGE036
wherein, the first and the second end of the pipe are connected with each other,
Figure 793665DEST_PATH_IMAGE037
、
Figure 940612DEST_PATH_IMAGE038
in order to be the weight coefficient,
Figure 437453DEST_PATH_IMAGE039
for concentricity, the following method is used:
Figure 353456DEST_PATH_IMAGE040
wherein the content of the first and second substances,
Figure 277550DEST_PATH_IMAGE041
is the maximum value of the thickness of the foamed layer in each radial detection direction,
Figure 911793DEST_PATH_IMAGE042
is the minimum value of the thickness of the foamed layer in each radial detection direction.
3. The coaxial cable concentricity online compensation control method according to claim 1 or 2, wherein the step (4) maximizes the decision utility by using a DQN network.
4. The coaxial cable concentricity online compensation control method of claim 3, wherein the strategy is
Figure 212325DEST_PATH_IMAGE043
Is in a given state
Figure 982835DEST_PATH_IMAGE045
Selecting an action
Figure 77829DEST_PATH_IMAGE047
Function of probability with the goal of maximizing time from
Figure 199369DEST_PATH_IMAGE049
Starting the value of the prize accumulated in the previous preset time period;
on-policy
Figure 38012DEST_PATH_IMAGE043
Down-defined action value function
Figure 663029DEST_PATH_IMAGE050
The following were used:
Figure 194504DEST_PATH_IMAGE051
wherein the content of the first and second substances,
Figure 537761DEST_PATH_IMAGE052
a discount factor is indicated in the form of a discount,
Figure 445674DEST_PATH_IMAGE053
is a mathematical expectation;
optimal action value
Figure 925197DEST_PATH_IMAGE054
The following equation is satisfied:
Figure 627574DEST_PATH_IMAGE055
in learning algorithms, the invention uses
Figure 455197DEST_PATH_IMAGE056
Is/are as follows
Figure 901222DEST_PATH_IMAGE058
To estimate an optimal action value function
Figure 235251DEST_PATH_IMAGE059
，
Figure 108529DEST_PATH_IMAGE058
The table update rules are as follows:
Figure 371147DEST_PATH_IMAGE002
wherein the content of the first and second substances,
Figure 410514DEST_PATH_IMAGE061
is the learning rate.
5. The coaxial cable concentricity on-line compensation control method of claim 1, wherein the number of radial detection directions
Figure 130209DEST_PATH_IMAGE062
Number of discharge ports
Figure 174388DEST_PATH_IMAGE063
。
6. A coaxial cable concentricity online compensation control system is characterized by comprising:
the device comprises an X-ray deviation measuring instrument, a water tank monitoring module, a discharge port pressure detection module, a decision control module and an extruder head electric control module;
the X-ray polarization measuring instrument is used for monitoring and acquiring the thickness of the foaming layer in each radial detection direction of the X-ray polarization center detector and combining the foaming layer into an eccentric state
Figure 713954DEST_PATH_IMAGE002
And submitted to the decision control module to be recorded
Figure 501781DEST_PATH_IMAGE064
In which
Figure 75982DEST_PATH_IMAGE066
For the number of radial detection directions,
Figure 291063DEST_PATH_IMAGE067
is a first
Figure 52345DEST_PATH_IMAGE069
The thickness of the foaming layer measured in the radial direction,
Figure 643864DEST_PATH_IMAGE070
；
the water tank monitoring module is used for monitoring and acquiring the water temperature of the hot water tank
Figure 72571DEST_PATH_IMAGE072
Water flow velocity
Figure 458553DEST_PATH_IMAGE074
Combined into a hot water tank
Figure 238290DEST_PATH_IMAGE013
And submitted to the decision control module to be recorded
Figure 102341DEST_PATH_IMAGE075
；
The discharge port pressure detection module is used for monitoring and acquiring electric control pressure values of all pressure discharge ports of the foaming material extruder to form a discharge port state
Figure 385555DEST_PATH_IMAGE023
And submitted to the decision control module to be recorded
Figure 208017DEST_PATH_IMAGE076
In which
Figure 209471DEST_PATH_IMAGE078
The number of the discharge holes is the same as that of the discharge holes,
Figure 877213DEST_PATH_IMAGE079
is as follows
Figure 14933DEST_PATH_IMAGE081
The electric control pressure value of the discharge hole,
Figure 8297DEST_PATH_IMAGE082
；
the decision control module is used for acquiring the eccentric state
Figure 497047DEST_PATH_IMAGE002
State of hot water tank
Figure 968480DEST_PATH_IMAGE013
State of discharge port
Figure 957777DEST_PATH_IMAGE084
Adopting a model-free reinforcement learning algorithm based on the Markov process to decide the next time slot
Figure 122042DEST_PATH_IMAGE085
Pressure discharge hole electric control signal
Figure 832509DEST_PATH_IMAGE086
And submitting to an extruder head electric control module; electric control signal of discharge port
Figure 107633DEST_PATH_IMAGE086
Is composed of the electric control signal values of all pressure discharge ports and is recorded as
Figure 485524DEST_PATH_IMAGE087
。
7. The system for online compensation and control of concentricity of coaxial cable according to claim 6, wherein the decision control module employs a markov process-based model-free reinforcement learning algorithm, and the algorithm comprises:
status of state
Figure 289532DEST_PATH_IMAGE089
Defined as an eccentric state
Figure 752875DEST_PATH_IMAGE002
State of hot water tank
Figure 566110DEST_PATH_IMAGE013
State of discharge port
Figure 798508DEST_PATH_IMAGE090
Aggregate, written as:
Figure 773417DEST_PATH_IMAGE091
；
action
Figure 724056DEST_PATH_IMAGE092
Is defined as an electric control signal of the pressure discharge hole and recorded as
Figure 340982DEST_PATH_IMAGE093
；
Reward function
Figure 427887DEST_PATH_IMAGE094
The method is defined as the weighted sum of the concentricity and the negative value of the change rate of the electric control pressure value of each discharge port, and comprises the following steps:
Figure 104856DEST_PATH_IMAGE095
wherein, the first and the second end of the pipe are connected with each other,
Figure 214894DEST_PATH_IMAGE096
、
Figure 104353DEST_PATH_IMAGE097
in order to be a weight coefficient of the image,
Figure 780185DEST_PATH_IMAGE098
for concentricity, the following method is used for calculation:
Figure 362476DEST_PATH_IMAGE099
wherein, the first and the second end of the pipe are connected with each other,
Figure 22127DEST_PATH_IMAGE100
the maximum value of the thickness of the foamed layer in each radial detection direction,
Figure 980856DEST_PATH_IMAGE101
is the minimum value of the thickness of the foamed layer in each radial detection direction.
8. The coax concentricity online compensation control system of claim 6 or claim 7, wherein the decision control module maximizes decision utility using a DQN network.
9. The coaxial cable concentricity online compensation control system of claim 8, wherein the strategy is
Figure 776774DEST_PATH_IMAGE102
Is in a given state
Figure 795545DEST_PATH_IMAGE103
Selecting an action
Figure DEST_PATH_IMAGE104
Function of probability with the goal of maximizing the slave time
Figure DEST_PATH_IMAGE106
Starting the value of the prize accumulated over the previous preset time period;
on-policy
Figure 142825DEST_PATH_IMAGE107
Down-defined action value function
Figure DEST_PATH_IMAGE108
The following were used:
Figure 374087DEST_PATH_IMAGE109
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE110
a discount factor is indicated in the form of a discount,
Figure DEST_PATH_IMAGE112
is a mathematical expectation;
optimal action value
Figure 962194DEST_PATH_IMAGE113
The following equation is satisfied:
Figure 151867DEST_PATH_IMAGE114
in learning algorithms, the invention uses
Figure DEST_PATH_IMAGE115
Is/are as follows
Figure 254952DEST_PATH_IMAGE116
To estimate an optimal action value function
Figure 555483DEST_PATH_IMAGE117
，
Figure 325993DEST_PATH_IMAGE116
The table update rules are as follows:
Figure 463735DEST_PATH_IMAGE004
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE119
is the learning rate.
10. The coaxial cable concentricity on-line compensation control system of claim 6, wherein the number of radial detection directions
Figure 276949DEST_PATH_IMAGE120
Number of discharge ports
Figure DEST_PATH_IMAGE121
。
CN202211194458.6A 2022-09-29 2022-09-29 Coaxial cable concentricity online compensation control method and system Active CN115274218B (en)

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EP2752287A1 (en) * 2013-01-02 2014-07-09 Proton Products International Limited Apparatus for measuring industrial products manufactured by extrusion techniques
CN209947528U (en) * 2019-07-04 2020-01-14 山东昆嵛电缆有限公司 Insulating eccentricity control device of well low voltage crosslinked cable of cable manufacture usefulness
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